This is how AI powers content recommendation

Mar 5th 2024ai

Imagine that a close friend has just given you a soppy romance novel as a birthday gift. You’re disappointed, as your reading preferences lie more along the lines of gritty James Chandler-esque thrillers. Actually, you realize, disappointed is a polite way of putting it. This genre is so off base that you’re wondering if it’s a lazy re-gift or whether you even want to continue being pals with this so-called friend.

If you’re an online retailer, this same sinking feeling about missing the mark is what your shoppers could experience when your content recommendations seem wrong. If your suggestions miss the mark, shoppers are naturally bound to be dismayed, and they may follow up by jumping ship to a competitor, tanking your conversion rates in the process.

What if you had artificial intelligence (AI) working behind the scenes on your ecommerce website, continually curating personalized content recommendations for your shoppers? That would be a great move, as 71% of shoppers don’t want just decent suggestions, they fully expect state-of-the-art personalized experiences. In fact, many of them would be fairly disinterested without personalized recommendations, as they’ve become used to enjoying them.

How a recommendation system works

Content-based recommender systems are essentially algorithms designed to suggest results — everything from consumer products to TV shows — that are highly relevant and closely aligned with people’s unique preferences and interests. Depending on the type of business, recommended items could encompass consumer products, videos, news articles, social-media posts, and more.

AI algorithms personalize recommendations by matching user profiles and item attributes with desired outcomes such as views, clicks, add to carts, and purchases. A recommendation model analyzes a shopper’s past behavior to identify unique patterns and preferences. By matching detailed profiles with specific attributes of content items across thousands of interactions on a website or in an online store, the technology can then make accurate recommendations.

For example, a shopper showing interest in snakeskin boots might start getting product recommendations for the latest hot sellers or classic boot styles. If they then become more interested in leather boots, the recommendations could seamlessly adjust to this expanded retail awareness and show both types of boots.

The role of AI in content recommendations

AI enables recommendation systems to analyze vast amounts of user data, learn user preferences, and predict which content they’ll likely enjoy. The process involves several key techniques:

Data collection and analysis

Data collection and analysis is the cornerstone of AI-powered content recommendation systems, as without data, there would be no accurate recommendations. Collected data encompasses shopper interactions such as clicks, views, user likes (and dislikes), and purchase history. And there’s more: through the employment of sophisticated natural language processing (NLP) techniques, these systems go deeper, analyzing queries to unearth themes, sentiments, and other critical characteristics.

The AI technology then uses this data to understand exactly what the shopper wants. It considers the duration of interactions, the frequency of viewing similar content, and the context in which certain types of items are being viewed, such as the local weather.

For example, consider a shopper searching for running shoes. If they’re doing it in early January, they could be wanting to make good on a fitness-related new year’s resolution. The AI might respond by suggesting merchandisers’ new year’s–resolution promotions for fitness gear and health-oriented products. Given the time of year, the AI might recommend gear for cold weather, unless the searcher lives in Australia; then it would suggest warm-weather items.

This comprehensive approach to data analysis means that AI systems can construct detailed user-based profiles for both shoppers and content items that are dynamic and evolve with each interaction. Such a nuanced understanding of both shopper behavior and content allows AI-driven recommendation systems to offer highly targeted suggestions that improve engagement. And that’s critical, because without a tailored customer experience, 45% of shoppers in one survey said they’d be likely to take their business elsewhere.

Collaborative filtering

Recommendation engine collaborative filtering methods involve recommending items by first finding similarities between shoppers and items. It goes beyond identifying shared interests, employing sophisticated AI to analyze deeper patterns in data and looking at both user-item interactions and user-user similarities.

For instance, let’s take two shoppers, Sally and Harry. They both like Bose sound systems. In addition, Harry has a fascination with Samsung TVs and gadgets. So with these seemingly similar users, the system might also start recommending Samsung products to Sally. This proactive approach lets the content recommendation system introduce a particular user to products they haven’t yet discovered but, based on aggregated preferences of similar shoppers, could likely find intriguing. Dynamically broadening the content horizon in a personalized manner is bound to enrich the user experience, with multiple positive outcomes for both shoppers and retailers.

Content-based filtering

Content-based filtering intricately analyzes attributes of content in order to serve relevant recommendations. AI delves into all kinds of detail, employing sophisticated feature-extraction techniques. For example, if a shopper often views high-end digital cameras with features such as mirrorless technology, 4K video recording, and interchangeable lenses, the AI knows to not recommend just any old camera. It considers the shopper’s preference for advanced item features, plus their apparent brand preferences and even their estimated price ranges. The content recommendation system might then suggest the latest high-end camera models that match these criteria, maximizing the chances that the shopper could decide to buy.

Advanced algorithms discern a shopper’s preference for sophisticated and niche elements. These algorithms work by taking input, performing a series of predefined operations or instructions on it, and producing output. The effectiveness of a recommendation algorithm is determined by its ability to consistently produce the correct or expected output for a wide range of inputs. When it hits the nail on the head, shoppers receive suggestions that could delight them.

Predictive modeling

Predictive modeling in AI uses machine-learning algorithms to forecast future user behavior based on past interactions, enhancing content recommendations. This approach involves analyzing a shopper’s history of interactions, such as purchases and content viewed, to identify patterns and preferences.

For example, if someone often explores sci-fi movies on a site like Netflix, a movie recommender system might predict a budding interest in other genres such as horror, fantasy, and science documentaries. As the AI gathers more data over time, predictions become increasingly accurate, ensuring relevance. This dynamic process facilitates a highly personalized and adaptive consumer experience.

Continuous learning and adaptation

AI-driven recommendation systems embody a continuous learning process in which algorithms dynamically adjust to new data, refining content suggestions in real time to match evolving preferences and trends. These AI systems meticulously track changes, and through ongoing learning, become increasingly accurate. For example, if a shopper develops an interest in a luxury fashion brand like Prada, the AI can quickly note this and modify its future recommendations to highlight the brand.

Context-aware recommendations

To make seemingly intuitive recommendations, AI can also consider context such as time of day, location, and device type. For example, a shopper might be browsing leather jackets in a clothing retailer’s app. Context-aware recommendations functionality would take into consideration aspects such as limited-time and seasonal offers (e.g., Black Friday and Christmas). In this case, the shopper might be shown a recommendation for a leather jacket that matches the styles they like, is available in their size, and is discounted for the event or holiday. Implementing this dynamic adjustment to accommodate external factors, combined with an understanding of individual preferences and situational context, makes for an outstanding content discovery experience.

Deliver better recommendations with AI

As AI technology advances, different types of recommender systems are becoming all the more sophisticated. Plus, as the AI boosts user engagement through effective content targeting, it invariably enhances people’s perceptions of the retail platform or service.

Do you have the latest recommendation technology in place for your search and discovery? If not, we’d love to help you supercharge this functionality. When you’re ready to leverage AI-powered recommendations to create relevant content suggestions for your shoppers, please get in touch!